Support Vector Regression (svr)
Forecasting the Prices of TAIEX Options by Using Genetic Programming and Support Vector Regression
... and support vector regression (SVR) are utilized to resolve problems inherent in forecasting options prices, where the six basic factors in the B-S model and the other factors which might affect the ...
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Prodepth: predict residue depth by support vector regression approach from protein sequences only.
... Support vector machine is a sophisticated supervised machine learning technique that is built based on statistical learning theory [59,60] and has been widely used in the applications of ...that ...
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Forecasting bus passenger flows by using a clustering-based support vector regression approach
... Based on the preceding analyses, to adjust the fine charac- teristics of bus passenger flows, this paper introduces a novel forecasting model based on clustering and nonlinear simula- tion: an affinity propagation-based ...
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AADT prediction using support vector regression with data-dependent parameters
... A modified support vector regression (SVR) approach has been proposed for future-year AADT estimation. The modified SVR uses data-dependent parameters in order to reduce computational time and to ...
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Maximal Information Coefficient and Support Vector Regression Based Nonlinear Feature Selection and QSAR Modeling on Toxicity of Alcohol Compounds to Tadpoles of Rana temporaria
... a support vector regression (SVR) based backward elimination; 18 descriptors are finally selected via a forward selection process that integrated minimal redundancy maximal relevance (mRMR), MIC and ...
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THE SUPPORT VECTOR REGRESSION WITH THE PARAMETER TUNING ASSISTED BY A DIFFERENTIAL EVOLUTION TECHNIQUE: STUDY OF THE CRITICAL VELOCITY OF A SLURRY FLOW IN A PIPELINE
... a regression function, y = f ( x ), such that it accura- tely predicts the outputs { y } corresponding to a new set of input-output examples, {( x , y )}, which are drawn from the same underlying joint probability ...
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An Efficient and Simplified Model for Forecasting using SRM
... (Support Vector Machines), provides robust and accurate results, however it may require intense computation and other ...(Support Vector Regression) for forecasting the retail sales of ...
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Lat. Am. j. solids struct. vol.12 número2
... Support vector machine is based on learning method using training procedures (Sánchez ...or regression problems since it can model nonlinear data in high dimensional feature space applying kernel ...
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Kriging Analysis In The Spatial Domain For Dispersion Models
... γ h is a two parameter family of curves . It may be spherical, exponential, power, Gaussian, cubic, etc. ii) A new paradigm [3] analyzing and learning from data is called support vector machines (SVM). Now ...
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Predicting Market Impact Costs Using Nonparametric Machine Learning Models.
... and support vector regression, to predict mar- ket impact cost accurately and to provide the predictive model that is versatile in the number of ...
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Biased Support Vector Machines and Kernel Methods for Intrusion Detection
... Support for this research received from ICASA (Institute for Complex Additive Systems Analysis, a division of New Mexico Tech), a DOD IASP, and an NSF SFS Capacity Building grants are gratefully acknowledged. We ...
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Packet Classification using Support Vector Machines with String Kernels
... A kernel is a function that enables the support vector machine to linearly separate the data in a higher-dimensional space . Using a kernel function is similar to adding a trivial feature to the already ...
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A novel support vector machine-based approach for rare variant detection.
... Advances in next-generation sequencing technologies have enabled the identification of multiple rare single nucleotide polymorphisms involved in diseases or traits. Several strategies for identifying rare variants that ...
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Programa computacional para a identificação automática de exoplanetas
... do support vector machine, entretanto a diferença entre ambas é pequena, menos de dez por cento, mostrando assim como a aplicação da técnica de gráficos de recorrência de fato acrescentou positivamente no ...
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Classification of Brain Tumor Using Support Vector Machine Classfiers
... Approaches used for classification falls into two categories. First category is supervised learning technique such as Artificial Neural Network (ANN), Support Vector Machine (SVM) and K-Nearest Neighbor ...
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River flow time series using least squares support vector machines
... Least squares support vector machines (LSSVM), as a modification of SVM was introduced by Suykens and Van- dewalle (1999). LSSVM is a simplified form of SVM that uses equality constraints instead of ...
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Utilização de Técnicas de Aprendizado de Máquina para Acompanhamento de Fóruns Educacionais
... Oliveira Júnior et. al. (2011) apresentam uma ferramenta de classificação automática de postagens em fóruns educacionais. As postagens podem ser classificadas como positivas ou negativas, utilizando algoritmos de ...
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A Survey on Potential of the Support Vector Machines in Solving Classification and Regression Problems
... and support vector machines have become the most popular learning from examples ...and regression tasks as well as to analyze the computational complexity corresponding to different methodologies ...
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A multi-label learning based kernel automatic recommendation method for support vector machine.
... When evaluating the performance of SVM with two different kernel functions on a given data set, there may not be large difference in terms of classification accuracy, but there may be significant difference between two ...
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A Computer Aided Diagnosis System for Lung Cancer Detection Using Support Vector Machine
... Abstract: Problem statement: Computer Tomography (CT) has been considered as the most sensitive imaging technique for early detection of lung cancer. Approach: On the other hand, there is a requirement for automated ...
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